تحلیل نظرات کاربران در فروشگاه دیجیکالا با هدف تشخیص نظرات فریبنده
الموضوعات : هوش مصنوعی و رباتیک
1 - دانشجو
2 - گروه آموزشی مدیریت کسب و کار، دانشکده مدیریت، علم و فنّاوری
الکلمات المفتاحية: تشخیص نظرات فریبنده, یادگیری ماشین, مدلهای زبانی بزرگ, تحلیل نظرات کاربران, شبکههای عصبی عمیق,
ملخص المقالة :
این پژوهش به بررسی و تحلیل نظرات کاربران در فروشگاه دیجیکالا با هدف تشخیص نظرات فریبنده پرداخته است. ابتدا دادههای نظرات کاربران جمعآوری و پیشپردازش شدند و سپس با استفاده از مدلهای مختلف یادگیری ماشین و مدلهای زبانی بزرگ، نظرات فریبنده تشخیص داده شدند. نتایج نشان داد که نظرات فریبنده معمولاً توسط کاربرانی با اعتبار پایینتر نوشته شدهاند و نظراتی که تعداد دیسلایک بیشتری دریافت کردهاند، اغلب دارای اعتبار کمتری هستند. همچنین، نظرات مثبت بیشترین تعداد را دارند و کاربران با نظرات مثبت اغلب لایکهای بیشتری دریافت میکنند. این پژوهش نشان داد که استفاده از مدلهای زبانی بزرگ و یادگیری ماشین میتواند به بهبود تشخیص نظرات فریبنده و افزایش دقت سیستمهای نظارت بر نظرات کاربران کمک کند و به شناسایی بهتر کاربران با ارزش و تاثیرگذار یاری رساند.
- Anderson ET, Simester DI (2014) Reviews without a purchase: Low ratings, loyal customers, and deception. J. Mark. Res. 51:249–269. https://doi.org/10.1509/jmr.13.0209
- Cao H (2020) Online review manipulation by asymmetrical firms: Is a firm’s manipulation of online reviews always detrimental to its competitor? Inf. Manag. 57:103244. https://doi.org/10.1016/j.im.2019.103244
- Chatterjee S, Goyal D, Prakash A, Sharma J (2021) Exploring healthcare/health-product e-commerce satisfaction: A text mining and machine learning application. J. Bus. Res. 131:815–825. https://doi.org/10.1016/j.jbusres.2020.10.043
- Chen J, Yang Y, Liu H (2021) Mining bilateral reviews for online transaction prediction: A relational topic modeling approach. Inf. Syst. Res. 32:541–560. https://doi.org/10.1287/ISRE.2020.0981
- Dong X, Lian Y (2021) A review of social media-based public opinion analyses: Challenges and recommendations. Technol. Soc. 67:101724. https://doi.org/10.1016/j.techsoc.2021.101724
- Ho SM, Hancock JT (2019) Context in a bottle: Language-action cues in spontaneous computer-mediated deception. Comput. Human Behav. 91:33–41. https://doi.org/10.1016/j.chb.2018.09.008
- Huang G, Liang H (2021) Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach. J. Bus. Res. 126:1–11. https://doi.org/10.1016/j.jbusres.2020.12.052
- Hussain S, Guangju W, Jafar RMS, Ilyas Z, Mustafa G, Jianzhou Y (2018) Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Comput. Human Behav. 80:22–32. https://doi.org/10.1016/j.chb.2017.09.019
- Ke Z, Liu D, Brass DJ (2020) Do online friends bring out the best in us? The effect of friend contributions on online review provision. Inf. Syst. Res. 31:1322–1336. https://doi.org/10.1287/isre.2020.0947
- Kumar N, Venugopal D, Qiu L, Kumar S (2019) Detecting anomalous online reviewers: An unsupervised approach using mixture models. J. Manag. Inf. Syst. 36:1313–1346. https://doi.org/10.1080/07421222.2019.1661089
- Kumar N, Venugopal D, Qiu L, Kumar S (2018) Detecting review manipulation on online platforms with hierarchical supervised learning. J. Manag. Inf. Syst. 35:350–380. https://doi.org/10.1080/07421222.2018.1440758
- Lamb Y, Cai W, McKenna B (2020) Exploring the complexity of the individualistic culture through social exchange in online reviews. Int. J. Inf. Manage. 54:102198. https://doi.org/10.1016/j.ijinfomgt.2020.102198
- Lee SY, Qiu L, Whinston A (2018) Sentiment manipulation in online platforms: An analysis of movie tweets. Prod. Oper. Manag. 27:393–416. https://doi.org/10.1111/poms.12805
- Lei Z, Yin D, Zhang H (2021) Focus within or on others: The impact of reviewers’ attentional focus on review helpfulness. Inf. Syst. Res. 32:801–809. https://doi.org/10.1287/ISRE.2021.1007
- Li L, Lee KY, Lee M, Yang SB (2020) Unveiling the cloak of deviance: Linguistic cues for psychological processes in fake online reviews. Int. J. Hosp. Manag. 87:102468. https://doi.org/10.1016/j.ijhm.2020.102468
- Li L, Yang L, Zhao M, Liao M, Cao Y (2022) Exploring the success determinants of crowdfunding for cultural and creative projects: An empirical study based on signal theory. Technol. Soc. 70:102036. https://doi.org/10.1016/j.techsoc.2022.102036
- Pang H, Liu J, Lu J (2022) Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat. Technol. Soc. 70:102004. https://doi.org/10.1016/j.techsoc.2022.102004
- Qiao D, Lee SY, Whinston AB, Wei Q (2020) Financial incentives dampen altruism in online prosocial contributions: A study of online reviews. Inf. Syst. Res. 31:1361–1375. https://doi.org/10.1287/isre.2020.0949
- Sporer SL (1997) The less travelled road to truth: verbal cues in deception detection in accounts of fabricated and self-experienced events. Appl. Cogn. Psychol. 11:373–397. https://doi.org/10.1002/(SICI)1099-0720(199710)11:53.0.CO;2-0
- Wang H, Du R, Shen W, Qiu L, Fan W (2022a) Product reviews: a benefit, a burden, or a trifle? How seller reputation affects the role of product reviews. MIS Q. Manag. Inf. Syst 46:1243–1272. https://doi.org/10.25300/misq/2022/15660
- Wang Q, Zhang W, Li J, Mai F, Ma Z (2022b) Effect of online review sentiment on product sales: The moderating role of review credibility perception. Comput. Human Behav. 133:107272. https://doi.org/10.1016/j.chb.2022.107272
- Wang Y, Goes P, Wei Z, Zeng D (2019) Production of online word-of-mouth: Peer effects and the moderation of user characteristics. Prod. Oper. Manag. 28:1621–1640. https://doi.org/10.1111/poms.13007
- Wu C, Mai F, Li X (2021) The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model. Inf. Manag. 58:103408. https://doi.org/10.1016/j.im.2020.103408
- Wu PF (2019) Motivation crowding in online product reviewing: A qualitative study of amazon reviewers. Inf. Manag. 56:103163. https://doi.org/10.1016/j.im.2019.04.006
- Xu W, Zhang C(2018) Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets Comput. Human Behav. 89:199–206. https://doi.org/10.1016/j.chb.2018.07.041
- Yang, Y, Zhang, K, 2022. sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Inf. Syst. Res. https://doi.org/10.1287/isre.2022.1124
- Yu Y, Khern-am-nuai W, Pinsonneault A (2022) When paying for reviews pays off: The case of performance-contingent monetary incentives. MIS Q. Manag. Inf. Syst 46:609–626. https://doi.org/10.25300/MISQ/2022/15488
- Zhang D, Zhou L, Kehoe JL, Kilic IY (2016) What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J. Manag. Inf. Syst. 33:456–481. https://doi.org/10.1080/07421222.2016.1205907
- Zhang W, Du Y, Yoshida T, Wang Q (2018) DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Inf. Process. Manag. 54:576–592. https://doi.org/10.1016/j.ipm.2018.03.007
- Zhang W, Xie R, Wang Q, Yang Y, Li J (2022) A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence. Decis. Support Syst. 157:113765. https://doi.org/10.1016/j.dss.2022.113765
- Zhou Y, Lv X, Wang L, Li J, Gao X (2023) What increases the risk of gamers being addicted? An integrated network model of personality–emotion–motivation of gaming disorder. Comput. Human Behav. 141:107647. https://doi.org/10.1016/j.chb.2022.107647
- Zhuang M, Cui G, Peng L (2018) Manufactured opinions: The effect of manipulating online product reviews. J. Bus. Res. 87:24–35. https://doi.org/10.1016/j.jbusres.2018.02.016
- Lakshami, S., Muthumani, S.(2017). Building Brand Power. IOP Conference Series: Materials Science and Engineering. 197, https://iopscience.iop.org/article/10.1088/1757-899X/197/1/01
